Awesome Data Science Resources

Statistics

• Multicollinearity check in R
• Data transformations
• Outlier
• Missing value
• Skewness vs Kurtosis
• Sampling for imbalanced sample
• Degrees of freedom
• T - test
• ANOVA
• AUC
• Levenes Test
• Introduction to Principal Component Analysis
• Mahalanobis distance
• 11 dimensionality reduction techniques

Data Science Interivew Tips

• Want to Become a Data Scientist
• Difference of Data Science, Machine Learning and Data Mining
• Data Science, Machine Learning, BI Explained in a Amazing Paragraphs
• What is Predictive Analytics

Feature Engineering

• Discover Feature Engineering
• Feature Engineering

Statistical Test Interpretations

• Minitab Express

Deep Learning

• Getting Started Tensorflow
• How to implement deep learning in r using keras and tensorflow
• Deep Learning Research
• Trending DS deep learning methods

R, Python and AutomML packages

• Data science ecosystem: R vs Python vs Substitutes
• R for ML
• PYCaret
• automl Frameworks
• Task scheduling with python
• R Studio Blog: r vs python
• Quora Blog: r vs python
• Why r for data science and not python
• XanderHorn autoML
• How can r users learn python for data science
• r numpy
• pandas vs datatable
• Equivalents in r python and perl
• Exploratory data analysis with r
• Exploratory data analysis eda
• Python standard env
• Python Read Write tables
• psycopg2 tutorial

Data Science Plots

• ggplot2 package
• How to add a background image to ggplot2 graphs
• 7 visualizations you should learn in r
• Analyzing the 8 best visualization techniques
• Data Visualization Techniques
• Plotly
• Matplotlib
• Matplotlib org
• Shiny vs dash a side by side comparison

R Shiny

• Making a shiny dashboard
• Creating reporting template
• Build your first web app
• Shiny

ML Classifiers

Decision Tree

• Decision tree classifier implementation in r
• Code for decision tree python from scratch

Random Forest

• machine learning random forest from scratch with python
• Random forests from scratch
• An implementation and explanation of the random forest in python
• Understanding random forest and hyper parameter tuning
• Bootstrapping and oob samples in random forests
• Hyperparameter tuning the random forest
• Random forests h2o

GBM

• gradient-boosting

XGBoost

• XGBoost read the docs
• XGB github
• Introduction to XGBoost
• Ensemble r machine learning
• Ensemble learning
• XGBoost tuning of regularization
• XGBoost algorithm
• Fraud Detection

LightGBM

• LGBM - Laurae
• Lightgbm Parameters Guide
• Talkingdata adtracking fraud detection
• L1 and L2 Regularisation
• Microsoft malware prediction
• titanic voting pipeline stack and guide

GAM model

• Generalized Additive Models
• intro to GAM
• GAM
• GAM compare models
• Git - generalized additive models

Text Mining

• Text Classification
• Sentiment analysis countvectorizer TF IDF
• Sentiment analysis TF IDF
• Transformers bert roberta
• Roberta fastai huggingface transformers
• Transformer with LSTM
• How to preprocessing for glove part1 eda
• How to preprocessing for glove part2 usage
• how to preprocessing when using embeddings

Cohort Analysis

• What is cohort analysis and how should i use
• A beginners guide to cohort analysis
• Cohort and multi touch attribution
• What can you do with a cohort analysis
• How to use cohort data to analyze user behavior
• Benefits of performing a cohort analysis
• RFM segmentation
• Behavioral Cohorts
• Cohorts git

Time Series

• Uber Orbit python library
• Facebook releases prophet its free forecasting tools for python and r
• Weather forecast with regression models
• ARIMA model statsmodels python
• Time series analysis
• sklearn model selection TimeSeriesSplit
• Ensemble of trees for forecasting time series
• TSrepr time series representations
• Time series analysis using ARIMA model in r
• ARIMA model time series forecasting python
• Time series model of forecasting future power demand
• Timeseries classification
• Statsmodels tsa ARIMA
• Prediction task with Multivariate Time Series and VAR model
• NEURAL NETWORKS for algorithmic trading
• Awesome deep trading
• PAA
• Web traffic time series forecast

Model Interpreations

• ML understanding using R
• Gitpage - Shapwaterfall
• python - Shapwaterfall
• SHAP

Clustering

• Clustering a tutorial for cluster analysis with r
• The 5 clustering algorithms data scientists need to know

Customer Churn Predictions

• Why churn
• Customer churn prediction for subscription businesses using machine learning
• Customer churn Logistic Regression with R
• Churn classification
• Survival prediction using cost sensitive learning
• 6 factors to consider before building a predictive model for life insurance
• Predicting customer churn
• Project - detecting early alzheimer
• What drives b2b customer attrition
• Define customer churn b2b
• Churn Rate
• Identify churn at risk customers
• Customer retention metrics
• New customers vs return customers
• Customer purchase behaviour analytics

Deep Learning Method

• Churn vector embedding
• PDF arxiv
• ProfLogit for Customer Churn Prediction

Web Scraping

• Web Scraping Machine Learning using python
• Web Scraping Hackmageddon
• Web Scraping Data Preprocessing machine learning model
• Recommender systems in python
• Content based filtering

Data Science Cheat Sheets

• Data Science Cheat sheets from kaggle.com

ML and heuristic data labeling

• How we used ml and heuristic data labeling to help customers with their cloud migration

Further Reading

• Arun Jagota - Published in Towards Data Science
• List of awesome ML Learning
• BERT
• PDF table extractions
• SQL

R package Development

Useful web links

1. [quantide - Chapter 6

   Creating R packages](http://www.quantide.com/ramarro-chapter-06/)

2. analyticsvidhya - How I created a package in R & published it on CRAN / GitHub
3. hvitfeldt - usethis-workflow-for-package-development
4. kbroman - Writing vignettes
5. r-pkgs - Releasing a package
6. r-bio - An introduction to Git and how to use it with RStudio
7. stackoverflow - R CMD check –as-cran warning
8. mjdenny - R Package Development Pictorial

Code snippet to submit library to CRAN

Build package

devtools::document(roclets=c('rd', 'collate', 'namespace', 'vignette'))
devtools::build()
devtools::use_news_md()
devtools::use_code_of_conduct()
devtools::use_cran_badge()
devtools::use_cran_comments()

Upload it to CRAN

devtools::submit_cran()

Create websites for you and your projects using gitpage

• pages.github.com

Developing github web page using below package

• pkgdown

pkgdown::build_site().

Write R markdown

1. basic writing and formatting syntax
2. pdf document format
3. authoring basics
4. kableExtra
5. knitr-markdown
6. rmarkdown.rstudio.com

Create your own R Package Logo

• shixiangwang.github

library(hexSticker)
library(UCSCXenaTools)
library(extrafont)
font_import()
loadfonts(device = "win")

library(showtext)
## Loading Google fonts (http://www.google.com/fonts)
font_add_google("Archivo Black", "arb")

imgurl <- image_read("man/figures/dml_icon3.png")

## Design Stickers
sticker(imgurl,
        s_width = 1,
        s_height = 1.2,
        package="MyRpackage", p_size=20, s_x=1, s_y=.75,
        url = "https://CRAN.R-project.org/package=MyRpackage",
        u_color = "white", u_size = 3,
        h_fill="dodgerblue4", h_color='blue',
        p_family ="arb",
        filename="man/figures/dml_logo.png")

Get the Number of Downloads of your R package from CRAN

For examples to check the number of downloads of ‘data.table’ R package

1. Month - https://cranlogs.r-pkg.org/badges/data.table
2. Day - https://cranlogs.r-pkg.org/badges/last-day/data.table
3. Total - https://cranlogs.r-pkg.org/badges/grand-total/data.table